Tight and Efficient Gradient Bounds for Parameterized Quantum Circuits

The training of a parameterized model largely depends on the landscape of the underlying loss function. In particular, vanishing gradients are a central bottleneck in the scalability of variational quantum algorithms (VQAs), and are known to arise in various ways. However, a caveat of most existing...

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Veröffentlicht in:	arXiv.org 2024-09
Hauptverfasser:	Letcher, Alistair, Woerner, Stefan, Zoufal, Christa
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Circuit design Circuits Concentration gradient Discriminators Generative adversarial networks Lower bounds Machine learning Parameterization Physics - Quantum Physics Qubits (quantum computing) Training
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